Electrical resistivity as a descriptor for classification of amorphous versus crystalline phases of alloys. (1st June 2022)

• Record Type: Journal Article
• Authors: You, Daegun; Zhang, Haitao; Ganorkar, Shraddha; Kim, Taeyeop; Schroers, Jan; Vlassak, Joost J.; Lee, Dongwoo
• Is Part Of: Acta materialia. Volume 231(2022)
• Publication Date: 2022-06-01
• Subjects: Combinatorial synthesis -- High-throughput experiment -- Machine learning -- Crystallinity -- Metallic glasses; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2022.117861
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: Discovering new metallic glasses, non-crystalline alloys with unique combinations of mechanical and chemical properties, is a challenging endeavor because it requires exploration of a vast composition space. High-throughput experiments have greatly enhanced the efficiency with which composition-dependent properties of potential glass-forming alloys can be measured, but phase identification remains a bottleneck because slow or expensive techniques such as table-top or synchrotron-based X-ray diffraction measurements are required. In this study, we developed machine learning (ML) models that can classify amorphous and crystalline phases of alloys using electrical resistivity as a primary descriptor. Artificial neural networks were constructed to correlate the electrical resistivities and the X-ray diffractograms of a broad range of combinatorially synthesized alloys. The ML models are found to classify amorphous/crystalline phases in both thin-film libraries and bulk alloys with high accuracy. Graphical abstract: Image, graphical abstract